cum.residuals {timereg} | R Documentation |
Computes cumulative residuals and approximative p-values based on resampling techniques.
cum.residuals(object,data=sys.parent(),modelmatrix=0,cum.resid=0, n.sim=500,weighted.test=1,start.design=1)
object |
an object of class 'aalen', 'timecox', 'cox.aalen' where the residuals are returned ('residuals=1') |
data |
data frame based on which residuals are computed. |
modelmatrix |
specifies a grouping of the data that is used for cumulating residuals. Must have same size as data and be ordered in the same way. |
n.sim |
number of simulations in resampling. |
weighted.test |
to compute a variance weighted version of the test-processes used for testing constant effects of covariates. |
cum.resid |
to compute residuals versus each of the continuous covariates in the model. |
start.design |
if '1' the groupings specified in modelmatrix changes over time, i.e. in the case with time-dependent covariates. |
returns an object of type "cum.residuals" with the following arguments:
cum |
cumulative residuals versus time for the groups specified by modelmatrix. |
var.cum |
the martingale based pointwise variance estimates. |
robvar.cum |
robust pointwise variances estimates of cumulatives. |
obs.testBeq0 |
observed absolute value of supremum of cumulative components scaled with the variance. |
pval.testBeq0 |
p-value covariate effects based on supremum test. |
sim.testBeq0 |
resampled supremum value. |
conf.band |
resampling based constant to construct robust 95% uniform confidence bands for cumulative residuals. |
obs.test |
absolute value of supremum of observed test-process. |
pval.test |
p-value for supremum test statistic. |
sim.test |
resampled absolute value of supremum cumulative residuals. |
proc.cumz |
observed cumulative residuals versus all continuous covariates of model. |
sim.test.proccumz |
list of 50 random realizations of test-processes under model for all continuous covariates. |
Thomas Scheike
Martinussen and Scheike, Dynamic Regression Models for Survival Data, Springer (2006).
library(survival) data(sTRACE) # Fits Aalen model and returns residuals fit<-aalen(Surv(time,status==9)~age+sex+diabetes+chf+vf, sTRACE,max.time=7,n.sim=0,residuals=1) # constructs and simulates cumulative residuals versus age groups fit.mg<-cum.residuals(fit,sTRACE,n.sim=200, modelmatrix=model.matrix(~-1+factor(cut(age,4)),sTRACE)) par(mfrow=c(1,4)) # cumulative residuals with confidence intervals plot(fit.mg); # cumulative residuals versus processes under model plot(fit.mg,score=1); summary(fit.mg) # cumulative residuals vs. covariates Lin, Wei, Ying style fit.mg<-cum.residuals(fit,sTRACE,cum.resid=1,n.sim=200) par(mfrow=c(2,4)) plot(fit.mg,score=2) summary(fit.mg)